1.

Use the​ year/subway fare data shown below. Let x represent the​ year, with 1960 coded as​ 1, 1973 coded as​ 14, and so on. Let y represent the subway fare. Does the best model appear to be a good​ model? Why or why​ not? Using the best​ model, find the projected subway fare in the year

20102010.

Does the best model appear to be a good​ model? Why or why​ not?

The best model is the ____ which does not appear

to be a good model because its coefficient of determination is R2 equals=

2

The data show systolic and diastolic blood pressure of certain people. Find the regression​ equation, letting the first variable be the independent​ (x) variable. Find the best predicted diastolic pressure for a person with a systolic reading of

113113.

Use a significance level of 0.05.

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Click the icon to view the critical values of the Pearson correlation coefficient r.

What is the regression​ equation?

3.

isted below are the budgets​ (in millions of​ dollars) and the gross receipts​ (in millions of​ dollars) for randomly selected movies. Answer parts

​a-c.

Click here to view a table of critical values for the correlation coefficient.

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a. Find the value of the linear correlation coefficient r.

r= __________

​(Round to three decimal places as​ needed.)

1. Wageweb conducts surveys of salary data and presents summaries on its website. Based on salary data as of October 1, 2002, Wageweb reported that the average annual salary for sales vice presidents was $142,111, with an average annual bonus of $15,432. Assume the following data are a sample of the annual salary and bonus for 10 sales vice presidents. Data are in thousands of dollars. Vice President Salary Bonus 1 135 12 2 115 14 3 146 16 4 167 19 5 165 22 6 176 24 7 98 7 8 136 17 9 163 18 10 119 11 a) Develop a scatter diagram for these data with salary as the independent variable, x and bonus as the dependent variable, y. b) What does the scatter diagram developed in part a) indicate about the relationship between salary and bonus? c) Use the least squares method to develop the estimated regression equation. We can call intercept for the regression line, b0 and slope of the regression line, b1. d) Provide an interpretation for the slope of the estimated regression equation. e) Predict the bonus for a vice president with an annual salary of $120,000. f) Compute SST, SSR, and SSE g) Compute the coefficient of determination r2. Comment on the goodness of fit. h) What is the value of the sample correlation coefficient? i) Develop the null and alternative hypothesis to test the linear relationship between salary and bonus j) At the .05 level of significance, determine whether salary and bonus are linearly related. Use the t test. k) Solve the problem in Excel and compare your results.

esults for the fourth quarter of 2019 are provided below. CVI's management is concerned as to why the operating income was lower than budgeted. 2019 fourth-quarter operating statement Actual Budget Revenues: High-speed Internet service $1,822,800 $1,890,000 Regular-speed Internet service 2.856.000 2.646.000 4,678,800 4,536,000 Expenses Billing and collection (55 per customer per quarter) 226,800 210.000 Variable costs of high-speed service ($15 per customer per quarter) 176,400 189.000 Variable costs of regular-speed service ($5 per customer per quarter) 168,000 147.000 Fixed costs 2.650.000 2.300.000 3221 2002 846 000 Operating income $1.457 600 $1.690.000 The budget was based on CVi holding a 35% market share assuming a total budgeted market size of 120.000 customers. The actual market size for the fourth quarter of 2019 turned out to be 125.000 customers, due to new apartment buildings in the area. The budget also assumed that 30% of CVI's customers would select the high-speed package and the remaining 70% of CVI's customers would select the regular-speed package CVI's high-speed package was budgeted with a selling price of $150 per customer per quarter. The regular-speed package had a budgeted selling price of $90 per customer per quarter. The actual prices in the fourth quarter were $155 and $85 per customer for the high-speed and regular-speed packages respectively.

Calculate each of the following variances:

a) Sales price variance

b) sales volume variance

c) Sales quantity variance

d) Sales mix variance

e) Market size variance

f) Market share variance

Studies have shown that people who suffer sudden cardiac arrest (SCA) have a better chance of survival if a defibrillator is administered very soon after cardiac arrest. How is survival rate related to the time between when cardiac arrest occurs and when the defibrillator shock is delivered? This question is addressed in the paper “Improving Survival from Sudden Cardiac Arrest: The Role of Home Defibrillators” (by J.K. Stross, University of Michigan, February 2002). The accompanying data give y = survival rate (percent) and x = mean call-to-shock time (minutes) for a cardiac rehabilitative center (where cardiac arrests occurred while victims were hospitalized and so the call-to-shock time tended to be short) and for four communities of different sizes

Mean call-to-shock time,x                 2              6              7              9              12

Survival Rate, y                                   92............ 44 .............32 ...............6 .................4

Do the following by hand and on Minitab.

1)Construct a scatter plot.

2)Calculate the Pearson correlation coefficient.

3)Determine equation of least squares line that can be used for predicting a value of y based on a value of x.

4)Compute SSE = for the least squares line.

5)Why do we call the least squares line the “best fitting line”?

6)   Calculate r² using the following formula: .   Interpret the r² value.

7) Using your equation in part c, draw the least squares line on the scatterplot you constructed in part a.

8) Use your prediction equation to predict SCA survival rate for a community with a mean call-to-shock time of 8 min. (Round your answer to five decimal places.)

1. (20 pts) Use the “Distance.sav” (SPSS) data set (located below) to perform a linear regression analysis. This dataset shows how far on average a person in Illinois drives each year. Write your findings using the format presented in the class slides. (2 pts) How much of the variation in the dependent variable is explained by the variation in the independent variable? What statistic did you use? (2 pts) Is the linear model significantly different than zero? Why or why not? What statistic did you use? (4 pts) Do the model assumptions hold? Why or why not? Provide a thorough response. (Hint: Create both a Histogram and a Normal probability plot using the standardized residuals. Also create a scatterplot of Regression Standardized Residuals and the Regression Standardized Predicted Value of the dependent variable. Attach a copy of each below.)

2. Enter the following record into the Inventory table using data listed below:

3. The Chevrolet Lumina was sold on 5/20/08 by Bauer for $9,300. Update the INVENTORY table and modify date sold, selling price, and repnumber.
4. Write SQL statements to query the following information
   1. All vehicles available for sale with a selling price less than $6,000, sorted by cost in descending order.
   2. All vehicles sold, grouped by Sales Rep.
   3. All vehicles made by Ford and Toyota.

(5 Questions are the end of the article. please I need the answer)

U.S. Factory Sector Clocks Strongest Growth in 14 Years

Analysts had expected a slowdown given rising trade tensions

By Sharon Nunn

WASHINGTON—American factory activity in August expanded at the strongest pace in more than 14 years, despite rising tensions with some of the U.S.’s largest trade partners.

The Institute for Supply Management on Tuesday said its manufacturing index rose to 61.3 in August, the highest level since May 2004, from 58.1 in July. Sales of factory-made products, or new orders, output and employment all grew at a faster pace in August.

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Tuesday’s release surprised analysts who had expected a slowdown in the industry in light of rising trade tensions and a typically weaker month for factory activity. Economists surveyed by The Wall Street Journal had expected a 57.5 reading for August.

“Despite concerns over U.S. protectionist policies, manufacturing sentiment remains on a solid footing, supported in large part by firm domestic demand,” said Pooja Sriram, U.S. economist at Barclays.

The U.S. and Europe, China and other countries are in the midst of trade battles stemming from steel and aluminum tariffs the Trump administration enacted earlier this year.

Mohamed A. El-Erian, chief economic adviser at Allianz, tweeted, “In addition to highlighting the strength of the U.S. #economy, this also points to the more general theme of divergence in advanced countries’ economic performance and policies.”

Though most economists hailed Tuesday’s report as a sign of robust growth continuing into the second half of 2018, some analysts said there are signs of overheating in the manufacturing industry.

“The last time we have seen something akin to the current run late in an expansion occurred in” the late 1980s, when the Federal Reserve had to raise the fed funds target rate to almost 10% to tamp down inflation, according to Stephen Stanley, chief economist at Amherst Pierpont Securities. “If you want to conclude from this quick history lesson that the Fed is currently too easy and in the process of making a policy mistake, I would not object.”

Most private economists expect the Fed will raise short-term interest rates two more times this year, once in September and again in December, with strong economic data continuing to roll into the summer months.

Despite the headline growth in factory activity, there are latent signs recent trade actions may be beginning to take a toll. An underlying gauge of new export orders for primary metals, transportation equipment and machinery declined in August, with machinery last declining at the beginning of 2017.

“We’re a significant exporter of railcars, airplanes, automobiles…Machinery is our number 6 industry sector,” said Tim Fiore, who oversees the ISM survey of factory purchasing and supply managers. “If export markets are closed off to us, orders will go down, [then] exports and production.”

Trade tensions, coupled with what appear to be economic slowdowns in some of the U.S.’s biggest trading partners, could be headwinds for the manufacturing sector.

Tuesday’s ISM report also showed a measure of inflation grew at a slower pace; the Backlog of Orders Index continued to expand, at higher levels compared with the previous month; and imports grew at a slower pace.

Broader economic growth picked up robustly in the second quarter after a modest slowdown in the early months of 2018. The unemployment rate declined below 4% this spring and forecasters expect solid growth this year, supported by recent tax cuts and strong consumer sentiment.

QUESTIONS: